Abstract
<p class="p1" style="margin-right: 0px; margin-bottom: 0px; margin-left: 0px; text-indent: 36px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 12px; line-height: normal; font-family: "Times New Roman";">Depression and anxiety are debilitating mental disorders that often co-occur. This comorbidity has prompted researchers to parse the shared and unique phenomenological features of these conditions. Affective models of psychopathology highlight heightened negative affectivity as one shared feature, but also suggest divergent affective patterns such that depression may be specifically characterized by lower positive affectivity and anxiety may be characterized by more physiologically aroused states. However, these clear predictions about differential patterns of emotions associated with depression and anxiety have not yet been adequately examined in day-to-day life, hindering our ability to identify those most at risk for experiencing these disorders. To clarify the specific patterns of emotion that relate to depression and anxiety, an affective network analysis was conducted with 144 undergraduates that completed ecological momentary assessments (EMA) of affect across two months. The network analysis characterized group-level and individual patterns of emotion connections both at the same time point and the previous time point. We tested individual differences in connections between emotions as predictors of depression and anxiety symptoms at the end of the semester, finding that both depression and anxiety symptoms were predicted by a weaker relationship between anxious and happy emotions over time. In contrast, only depression symptoms were predicted by weaker relationships between positive emotions (relaxed and content or happy) over time. These results provide partial support for traditional affective models of psychopathology and suggest potential patterns of real-world emotion associated with risk for depression and anxiety.</p>